# Inferential Statistics Flashcards

1
Q

Inferential Statistics

Criterion of “TRUTH”

A

Validity

2
Q

The percentage of people with the disease who are detected by the test

A

% SENSITIVITY

3
Q

TP ÷ [TP + FN] x 100

A

% SENSITIVITY

4
Q

% SENSITIVITY, higher the sensitivity the better?

A

ye

5
Q

what does % SENSITIVITY measures?

A

TRUE POSITIVE

yung mga tunay na may sakit if ever

6
Q

is the percentage of people with the disease who are not detected by the test, complement of sensitivity

A

% FALSE NEGATIVE

7
Q

FN ÷ [TP + FN] x 100

A

% FALSE NEGATIVE

8
Q

Counterpart of %sensitivity

A

% FALSE NEGATIVE

9
Q

T or F

Higher the sensitivity, the lower the false negative

A

T

inversely proportional sila with %sensitivity

10
Q

is the percentage of people without the disease who are correctly labelled by the test as not diseased.

A

% SPECIFICITY

11
Q

TN ÷ [FP + TN] x 100

A

% SPECIFICITY

12
Q

T or F

Higher the specificity the better – mababa false positive

A

T

13
Q

is the percentage of people without the disease who are incorrectly labelled by the test as having disease, complement of specificity.

A

% FALSE POSITIVE

inversely proportional with %specificity

14
Q

FP ÷ [FP + TN] x 100

A

% FALSE POSITIVE

15
Q

T or F

yes to false positive and false negative

A

F

NO DAPAT

16
Q

is defined as the likelihood that an individual with a positive test has the disease.

A

PREDICTIVE VALUE OF A POSITIVE TEST

17
Q

TP ÷ [TP + FP] x 100

A

PREDICTIVE VALUE OF A POSITIVE TEST

Lahat ng positive result to get who are TRULY POSITIVE

18
Q

is defined as the likelihood that a person with a negative test does not have the disease.

A

PREDICTIVE VALUE OF A NEGATIVE TEST

19
Q

TN ÷ [FN + TN] x 1004

A

PREDICTIVE VALUE OF A NEGATIVE TEST

All of the negative result to get who are FALSE NEGATIVE talaga

20
Q

The ratio of the chance of the test being positive if having the condition compared to the chance of testing positive if not having the condition

A

Positive Likelihood Ratio +LR

21
Q

The ratio of the chance of the test being negative if having the condition compared to the chance of testing negative in not having the condition.

A

Negative Likelihood ratio -LR

22
Q

if u see this card

A

practice the example of maam given for the Indices to Evaluate Accuracy of a Test or Diagnostic Examination

go na

23
Q

Also termed as “reproducibility” or “repeatability”

A

Reliability

Na ulit yung test then same result = reliability – CONSISTENT

Validity = nearest to true value

24
Q

Refers to the stability or consistency of information

A

Reliability

25
Q

The extent to which similar information is supplied when measurements are performed more than once.

A

Reliability

26
Q

T or F

A key goal in applied biostatistics is to make inferences about unknown population parameters based on sample statistics.

A

TRUE

27
Q

what is the difference for parameter and statistics when it comes to mean, SD, and Proportion

A

Paramerter = Population
Statistic = Sample

this means that kung anong TESTING used for sample, and popluation yun lang din gagamiting sa parameter

28
Q

There are two broad areas of statistical inference,

A
• Estimation
• Hypothesis Testing
29
Q

The process of determining a likely value for a population parameter (e.g., the true population mean or population proportion) based on a random sample.

A

Estimation – APPROXIMATION

30
Q

Estimation - T or F

In practice, we select a sample from the target population and use sample statistics (e.g., the sample mean or sample proportion) as estimates of the unknown parameter

A

T

31
Q

Estimation - T or F

The sample should be representative of the population, with participants selected at random from the population.

A

T

alam niyo nayan very ez

32
Q

Estimation - T or F

In generating estimates, it is also important to quantify the precision of estimates from different samples.

A

T

33
Q

Estimation

Point Estimate =

A

Single number

e.g.: 1, 2, and 69

34
Q

Estimation

Interval Estimate (Confidence Interval Estimate) =

A

may decimals (2 values lower and upper limit with confidence intervals)

35
Q

a range of values, derived from sample statistics, that is likely to contain the value of an unknown population parameter.

A

Confidence Interval

36
Q

Estimation - confidence interval

Because of their _ _ _ _ _ _ _ _ _ _ _ _ , it is unlikely that two samples from a particular population will yield identical confidence intervals.

A

Random Nature

37
Q

Estimation - confidence interval: T OR F

But if you repeated your sample many times, a certain percentage of the resulting confidence intervals would contain the unknown population parameter.

A

T

diko parin gets to

38
Q

If you see this card

A

go over the inferential statistics, check the estimation interval pls

39
Q

There are a number of population parameters of potential interest when one is estimating health outcomes (or “endpoints”).

A

Parameter Estimation

40
Q

Parameter Estimation

Many of the outcomes we are interested in estimating are either

A

continuous or dichotomous variables

, although there are other types.

41
Q

Parameter Estimation

The parameters to be estimated depend not only on whether the endpoint is continuous or dichotomous, but also on the ?

A

number of groups being studied.

42
Q

Parameter Estimation

When 2 groups are being compared what you need to establish between the groups?

A
• Independent (e.g., men versus women)
• Dependent (i.e., matched or paired, such as a before and after comparison).
43
Q

Parameters to estimate in health-related studies

One sample - Continuos varible

A

Mean

44
Q

Parameters to estimate in health-related studies

One sample - dichotomous variable

A

Proportion or Rate

yung mga prevelance,incidence rate …

45
Q

Parameters to estimate in health-related studies

2 Independent Samples - Cont. Variable

A

Difference in MEAN

46
Q

Parameters to estimate in health-related studies

2 Independents Samples - Dichoto. Variable

A

Difference in proportion or rates

pag 2 independent samples, lagi difference okay? okay

47
Q

Parameters to estimate in health-related studies

2 Dependent, Matched Samples - Cont. Variable

A

Mean Difference

iba ang difference in means sa mean difference okay? okay

48
Q

Confidence Intervals

Two types of estimated for each population parameter

A
• Point estimate
• Confidence interval (CI) estimate.
49
Q

What is the difference between Cont and Dichotomous Variable?

A

Cont is all about MEAN, while Dicho is proportions or rate

okay? OKAY

50
Q

Confidence Intervals

one first computes the point estimate from a sample?

A

Ye

para makuha mo Confidence intervals

51
Q

Confidence Interval - T or F

Sample means and sample proportions are unbiased estimates of the corresponding population parameters.

A

True

52
Q

If you see this card

A

go over the PRINCIPLES of confidence interval

need siya understood, not memorized

53
Q

The confidence interval estimate (CI) is a range of likely values for the population parameter based on

A

the point estimate, e.g., the sample mean

54
Q

Confidence Intervals Estimate - T or F

In practice, we select one random sample and generate one confidence interval, which may or may not contain the true mean. The observed interval may over- or underestimate μ.

A

True

55
Q

Confidence Intervals Estimate - T or F

The confidence interval does not reflect the variability in the unknown parameter.

A

T

56
Q

Confidence Intervals Estimate - T or F

what does confidence interval estimate REFLECTS

A

amount of random error in the sample and provides a range of values

likely to include the unknown parameter. sa range of values

57
Q

if u see this card

A

araling formula sa confidence interval

58
Q

Confidence Interval

For n >= 30, T or Z table?

A

Z table

59
Q

Confidence Interval

For n < 30

A

Use the t-table with df-n-1

60
Q

Point Estimate Z SE

where is the Z values got from?

A

the standard normal distribution for the selected confidence level

(e.g., for a 95% confidence level, Z=1.96).

61
Q

In practice, we often do not know the value of the population standard deviation (σ).

However, if the sample size is large (n > 30), then the sample standard deviations can be used to estimate the population standard deviation.

A

Point Estimate (+-) Z SE

62
Q

With smaller samples (n< 30) the Central Limit Theorem does not apply, and another distribution called

A

T-distribution

63
Q

Confidence Intervals Estimate for Smaller Samples

Similar to the standard normal distribution but takes a slightly different shape depending on the sample size.

A

T-distribution

64
Q

T-Distribution - T or F

In a sense, one could think of the t distribution as a family of distributions for larger samples.

A

F

smaller, n <30 - LESSSSSSSSSSSS THAN

65
Q

T - distribution - T or F

It produces smaller margins of error

A

F

It produces LARGER, because small samples are less precise

66
Q

t values are listed by?

A

degrees of freedom (df)

67
Q

T-Distribution - T or F

Just as with large samples, the t distribution assumes that the outcome of interest is approximately normally distributed.

A

T

68
Q

If u see this card

A

go over the example for Confidence intervals pls pls

PUHLEASE

69
Q

The sample proportion

A

p̂ (p hat)

70
Q

confidence interval can be computed by this

A

p hat formula

so go over it

71
Q

if u see this card

A

go over the example of Confidence Intervals Estimate for Population Proportion

72
Q

a contention or assumption made concerning a population characteristics.

A

STATISTICAL HYPOTHESIS

73
Q

. It is usually concerned with the parameters of the population about which the statement is made.

A

STATISTICAL HYPOTHESIS

NOT YET TRUE – ipprove palang

74
Q

The purpose of the research is to provide evidence to support or refute the null hypothesis

A

Hypothesis Testing

75
Q

Hypothesis testing comprises a set of what?

A

set of procedures

76
Q

Hypothesis Testing - T or F

A hypothesis is either rejected or not based on the probability of occurrence of the sample results if the null hypothesis were true.

A

T

77
Q

how is Statistical Hypothesis validated?

A

if calculated probability of results exceeds a prespecified value of alpha

78
Q

Hypothesis

If calculated probability is less than or equal to alpha?

A

hypothesis is rejected, therefore, result is statistically significant.

< (less than) = (equal) baka di mo alam eh

79
Q

This is the hypothesis of “no difference”. Statement of equality

A

Null Hypothesis (Ho)

80
Q

This is the hypothesis of “no relationship”.

A

Null Hypothesis (Ho)

81
Q

Asserts that population parameter is some value other than one hypothesized.

A

Alternative Hypothesis (H1 or HA)

82
Q

Usually the research hypothesis, the hypothesis the investigator believes in.

A

Alternative Hypothesis (H1 or HA)

83
Q

Null hypothesis should always be framed in hopes of being able to reject it so that the alternative hypothesis could be accepted.

A

oo

84
Q

Include values of statistics leading to rejection of null hypothesis. Usually called alpha or tail of the curve.

A

Critical Region or Region of Rejection

85
Q

These values are those whose probability of occurrence is less than (<) or equal to the level of sig/nificance, α.

A

Critical Region or Region of Rejection

86
Q

The probability level that is considered too low to warrant support of the hypothesis being tested.

A

Level of Significance or ALPHA Level

87
Q

Basis for inferring the operation of non-chance factors (0.05, 0.01, 0.1)

A

Level of Significance or ALPHA Level

omegaverse????

88
Q

if u see this card

A

MASTER the decision table

yung may Ho true, Ho false

89
Q

Region of Acceptance

alpha of the curve, greater than or equal to level of significance

A

1

90
Q

Region of Acceptance

When Ho is rejected?

A

statistically significant and the observed difference may not be attributed to sampling variation

91
Q

Region of Acceptance

If Ho is not rejected

A

ot statistically significant and may be due to sampling variation

92
Q

what is Ho?

A

null hypo

93
Q

When HA asserts that population parameter is different from one hypothesized (2-tailed test)

A

NON-DIRECTIONAL Ha

94
Q

Asserts the direction of the difference ( 1-tailed test)

A

DIRECTIONAL Ha

95
Q

Steps in Hypothesis Testing

1st step

A

Determine whether a 2-tailed or a 1-tailed test be made.

96
Q

Steps in Hypothesis Testing

2 step, what do you need to assume?

A

Ho and Ha

Null and Alternative

97
Q

Steps in Hypothesis Testing

3rd step, after the hypothesis

A

Choose alpha, the arbitrary level of significance

here is the basis of rejection AFTER the computation

98
Q
A
99
Q

Steps in Hypothesis Testing

4th and 5th step

A
1. Determine critical region
2. Determine appropriate test
100
Q

Steps in Hypothesis Testing

6th and 7th the last

A
1. Solution
2. Conclusion
101
Q

Conparison of Parameters or Indicators

Single Population

what interval/ration testi used

A

Z or T Test

102
Q

Conparison of Parameters or Indicators

Single Population

what ordinal test is used

A
• Kolmogorov
• SMirnov one sample test
103
Q

Conparison of Parameters or Indicators

Single Population

what nominal test is used

A
• Z test
• Chi Square Test
104
Q

Conparison of Parameters or Indicators

2 Population: Related Samples

what interval/ratio test is used

A

Paired t test

105
Q

Conparison of Parameters or Indicators

2 Population: Related Samples

what ordinal est is used

A
• Wilcoxon
• Matched pairs
• SIgned ranks test
106
Q

Conparison of Parameters or Indicators

2 Population: Related Samples

what nominal est is used

A

McNemar’s Test

107
Q

Conparison of Parameters or Indicators

2 Population: independent Samples

Interval/Ratio

A

Independent T-test

108
Q

Conparison of Parameters or Indicators

2 Population: independent Samples

Ordinal

A

Mann whitney U test

109
Q

Conparison of Parameters or Indicators

2 Population: independent Samples

Nominal

A
• fishers exact
• probability test
• Chi square test
110
Q

Conparison of Parameters or Indicators

3 or More population: Related samples

interval/ratio

A

F-test: 2 way analysis of Variance

111
Q

Conparison of Parameters or Indicators

3 or More population: Related samples

Ordinal

A

Friedman’s Analysis of Variance

112
Q

Conparison of Parameters or Indicators

3 or More population: Related samples

Nominal

A

Cochran’s Q test

113
Q

Conparison of Parameters or Indicators

3 or More population: Independent

Nominal

A

Chi square test

114
Q

Conparison of Parameters or Indicators

3 or More population: Independent

Ordinal

A

Kruskali wallis one way ANOVA

115
Q

Conparison of Parameters or Indicators

3 or More population: Independent

Interval/Ratio

A

F-Test: one way ANOVA

116
Q

Study of Relationship Between Variables

Interval/Ratio

A
• Regression
• Correlation
117
Q

Study of Relationship Between Variables

Ordinal

A
• Spearman Rank
• Correlation
• Coefficient
118
Q

Study of Relationship Between Variables

Nominal

A
• Kappa Test
• Contingenct
• Coefficient Test
119
Q

What statistical test

if u see this card

A

go over the relationship for the Independent and dependt and what statistical test will be used

120
Q

what statistical test is used when

• Independent - Qualitative
• Dependent - Qualitative
A

Chi square test

121
Q

what statistical test is used when

• Independent - Qualitative
• Dependent - Quantitative
A

T,Z, ANOVA

122
Q

what statistical test is used when

• Independent - Quantitative
• Dependent - Quantitative
A

Linear Regression

123
Q

what statistical test is used when

• Independent - Quantitative
• Dependent - Qualitative
A

Logistic Regression

124
Q

If u see this card

A

Please go over the CASE, example yan ha